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By Krysztof Huszcza
Two weeks ago, we published the Agent Baseline alongside Docker and Keycard. In it, we describe six security outcomes, 35 controls, and an open reference architecture for running AI agents at the enterprise level. Last week, we stress-tested it: we took it to a panel at Black Hat and spent about one hour being asked hard questions about it. Play Video: Snyk x Docker x Keycard | Agent Baseline Panel @ Black Hat 2026 The most useful question came from someone who had actually already read it.
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By Nuno Loureiro
Autonomous AI attacks have definitively moved from the research demos everyone's been awed by to standard operating procedure. For anyone paying enough attention, this is not necessarily news: the Five Eyes Alliance warned everyone back in June that AI will bypass cybersecurity in months, not years, with adversary breakout times that can now be measured in seconds. Gartner itself predicted something similar, expecting the window to exploitation to be cut in half as early as next year.
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By Snyk Team
Applications can change several times between scheduled security assessments. New features, APIs, and integrations may introduce risk long before the next annual penetration test begins. That gap is pushing offensive testing beyond a single tool or a single point-in-time engagement. Teams are increasingly combining Dynamic Application Security Testing (DAST), AI penetration testing, and AI red teaming to evaluate different layers of application risk.
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By Ranko Cupovic
When we started thinking about how to surface AI model risk inside Evo, the obvious answer was to borrow from how we score everything else: find the issue, assign a severity, surface it. Done. The core of the new approach is a real risk score, built the way security teams already reason about risk: Likelihood × Impact. Likelihood comes from Attack Success Rate (ASR), the share of real adversarial attacks that succeed against a model. Impact is how much damage the attacker's goal does when it lands.
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By Brendan Hann
The Remediation Agent and Malicious Code Defense are the first two pieces of Evo Agentic AppSec: security that not only surfaces risk, but resolves it and prevents the next ones. This morning, we announced the broadest expansion of the Snyk AI Security Platform to date: discover, remediate, validate, and prevent. A loop with a missing segment is not a loop; it is a gap that an autonomous attacker will occupy. Evo Continuous Offensive Security closes validation and shipped today.
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By Snyk Team
Building on our initial partnership that brought Snyk’s security intelligence into the Snowflake AI Data Cloud, we are taking the next step in securing the future of data-driven development. This new collaboration integrates Snyk Studio directly with Snowflake Cortex Code, ensuring that as organizations move their application logic to where their data lives, security remains an inherent part of the process rather than a secondary hurdle. Play Video: YouTube video 1.
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By Manoj Nair
A few months ago, I wrote that AI is building your attack surface faster than you can test it. I stand by every word I wrote then. But in the months since, after more than a hundred conversations with CISOs, CIOs, and CTOs across nearly every industry and geography, I've watched the picture get sharper, and a lot more urgent. The attack surface was only half the story, because the attacker profile has changed too.
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By Daniel Berman
Every so often, an industry gets a moment that quietly redraws where the line is — not because anything was said, but because something was proven. AI security had one of those moments last week, and it's worth being direct about that before getting into the details: this wasn't an incremental data point. It was the moment a risk that security and safety researchers had described in theory for years showed up, fully formed, in a disclosed incident report.
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By Snyk Team
AI pentesting (AI penetration testing) is the use of reasoning-capable AI models to autonomously find, exploit, and validate security vulnerabilities in running applications — especially the context-dependent flaws, such as broken authorization and business-logic abuse, that traditional scanners cannot detect.
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By Randall Degges
Here's a genuinely unsettling way to lose control of your laptop in 2026. You clone a normal-looking repo, ask your AI coding assistant to "set it up," and it writes an attacker's SSH key into your ~/.ssh/authorized_keys -- without ever really telling you that's what it did. No memory corruption, no zero-day, nothing clever. Just a file in the repo that wasn't the file it claimed to be. That attack is real, it's this week's news, and I'll walk through it. But the trick underneath is decades old.
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By Snyk
-OpenAI has completely overhauled its model naming system with the release of GPT-5.6, introducing three distinct tiers: Sol, Terra, and Luna. In this video, we put OpenAI's new flagship model, GPT-5.6 Sol, to the ultimate test. Using the Codex extension in VS Code, we throw our classic "Build me a secure notes app or I get fired" prompt at Sol. Watch as we break down the pricing and reasoning differences of the new tiers, run a full security audit using Snyk, and see if Sol's $5/$30 price tag is truly production-ready or if a small local CSRF bug gets us "fired" first.
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By Snyk
In this video, we break down why skipping code reviews is a massive mistake that will ultimately slow you down, leave you vulnerable, and compromise your system's accountability. We dive into three concrete reasons why reviewing AI-generated pull requests actually makes you a faster, safer developer, including a real-world story of a production bug caught in under 90 seconds. Resources Chapters.
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By Snyk
GLM 5.2 just launched from Z.ai, and it might be one of the biggest threats yet to the frontier model premium. It’s open, significantly cheaper than Claude Opus 4.8, and claims to deliver near-frontier coding performance across major benchmarks. But benchmarks only matter if the model can actually build something production-ready.
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By Snyk
In this video, we break down how to properly set up and use AI extension points - specifically MCP (Model Context Protocol) servers, Rules, Skills, and Hooks - to supercharge your development workflow. Using practical, security-flavored examples with Claude Code and Snyk, you'll learn how to configure a local project environment that automatically catches vulnerabilities before they ever hit your codebase. Whether you use the Claude CLI, VS Code extensions, or alternate AI ecosystems like Cursor or Gemini, you can use these exact steps as a blueprint to automate any workflow in your project.
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By Snyk
Over 78% of developers are using Claude for coding, but almost everyone is leaving its single most powerful feature switched off: Claude Skills. In this video, we break down what Claude Skills are, how they use "progressive disclosure" to keep your context window light, and the 7 best engineering skills you can install this week to completely supercharge your workflow.
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By Snyk
We put Anthropic’s new Claude Opus 4.8 to the test using our standard benchmark: building a secure, production-ready Notes app. Anthropic claims this model is four times less likely to let security flaws slip through. Operating on "Ultra Code" mode, the AI navigates environment blocks, writes its own E2E security test suite, and runs dependency audits. We walkthrough the final app and run a security scan using the Snyk CLI to see if Claude's code is truly safe to deploy.
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By Snyk
235,000 installs per week. That’s how quickly developers are downloading AI agent skills — packages that give AI coding agents new capabilities like shell access, file system operations, cloud access, and deployment permissions. But unlike traditional npm packages, agent skills introduce a completely new security problem: natural language instructions that AI agents can interpret and execute autonomously.
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By Snyk
On May 11, 2026, the TanStack namespace was hit by a "Mini Shai-Hulud" supply chain attack. Unlike typical attacks, this did not involve stolen credentials; instead, the threat group TeamPCP hijacked the legitimate GitHub Actions release pipeline. This video covers the technical details of the OIDC token extraction, the "Dead Man's Switch" that triggers a rm -rf / upon credential revocation, and the mandatory remediation order you must follow to save your data. We also discuss how to harden your workflow using release-age cooldowns and OIDC pinning.
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By Snyk
Are you confused by the terminology surrounding AI coding tools? You aren't alone. In this video, we break down the four essential components that transform a basic LLM into a powerful coding agent: Rules, Skills, Hooks, and the Model Context Protocol (MCP).
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By Snyk
GPT-5.5 vs Claude Opus 4.7 - two flagship AI models dropped one week apart, and both claim to be the best at agentic coding. We put that to the test by giving each model the exact same prompt: build a production-ready, secure note-taking application from scratch. But we didn't stop at reviewing the code. We actually tried to break it by running real security tests against each app to see whether AI-generated code can be trusted with user data. The results were not what we expected.
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By Snyk
This book will help both development and application security architects and practitioners address the risk of vulnerable open source libraries and discuss why such vulnerable dependencies are the most likely to be exploited by attackers.
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By Snyk
Forrester conducted a customer study to get insights into why organizations choose Snyk to help them tackle and implement developer-first security. Read the report to dive into the benefits, cost and value ROI for Snyk.
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By Snyk
This book reviews how the serverless paradigm affects the security of an application, and dives into the benefits it brings.
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By Snyk
Snyk's annual State of Open Source Security Report 2020 is here. Download it now to learn how Open Source security is evolving.
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By Snyk
"Shift left" has become the holy grail for security teams today but organizations are still struggling to successfully implement some of the key processes that shifting security left entails. A new study sponsored by Snyk and conducted by Enterprise Strategy Group (ESG) has found that while developers are indeed being given more responsibility for testing their applications for security issues, they simply don't have the knowledge or right set of tools to do so.
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By Snyk
81% of security and development professionals believe developers are responsible for open source security - but many organizations are still unsure how to start building a culture and practice of DevSecOps. Puppet & Snyk's study is digging deeper into the trends of DevSecOps adoption.
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By Snyk
The 2020 Gartner Market Guide for SCA is here! Recent Gartner survey finds that over 90% of organizations leverage OSS in application development - and as a result, security of open source packages was the highest ranked concern for respondents. These concerns have led to a growing market, addressed by various vendors for SCA tools that mitigate the risk of OSS. New trends emerge with devops on the rise - as the market shifts towards developer-friendly SCA tools.
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Snyk is an open source security platform designed to help software-driven businesses enhance developer security. Snyk's dependency scanner makes it the only solution that seamlessly and proactively finds, prioritizes and fixes vulnerabilities and license violations in open source dependencies and container images.
Security Across the Cloud Native Application Stack:
- Open Source Security: Automatically find, prioritize and fix vulnerabilities in your open source dependencies throughout your development process.
- Code Security: Find and fix vulnerabilities in your application code in real-time during the development process.
- Container Security Find and automatically fix vulnerabilities in your containers at every point in the container lifecycle.
- Infrastructure as Code Security Find and fix Kubernetes and Terraform infrastructure as code issues while in development.
Develop Fast. Stay Secure.